Papers for Relevance Assessment by Sharon Goldwater

Research Question: How can morphological information be used to improve the quality of statistical machine translation for highly-inflected languages?

Paper ID
(Link to PDF)

Title

Author(s)

P05-2016

Dependency-Based Statistical Machine Translation

Heidi Fox

P03-1051

Language Model Based Arabic Word Segmentation

Young-Suk Lee; Kishore Papineni; Salim Roukos; Ossama Emam; Hany Hassan

W02-0503

Acquisition System for Arabic Noun Morphology

Saleem Abuleil; Khalid Alsamara; Martha Evens

W02-0601

Unsupervised Learning of Morphology for Building Lexicon for a Highly Inflectional Language

Utpal Sharma; ugal Kalita; nd Rajib Das

J02-3002

Periods, Capitalized Words, etc.

Andrei Mikheev

E03-1007

Using POS Information for SMT into Morphologically Rich Languages

Nicola Ueffing, Hermann Ney

W05-0822

PORTAGE: A Phrase-Based Machine Translation System

Fatiha Sadat; Howard Johnson; Akakpo Agbago; George Foster; Roland Kuhn; Joel Martin; Aaron Tikuisis

W05-0709

The Impact of Morphological Stemming on Arabic Mention Detection and Coreference Resolution

Imed Zitouni; Jeffrey Sorensen; Xiaoqiang Luo; Radu Florian

W01-1407

Toward hierarchical models for statistical machine translation of inflected languages

Niessen, Sonja; Ney, Hermann

P05-1071

Arabic Tokenization, Part-of-Speech Tagging and Morphological Disambiguation in One Fell Swoop

Nizar Habash; Owen Rambow

P05-2012

Phrase Linguistic Classification and Generalization for Improving Statistical Machine Translation

Adria de Gispert

W02-0506

Building a Shallow Arabic Morphological Analyser in One Day

Kareem Darwish

P03-1040

Feature-Rich Statistical Translation of Noun Phrases

Philipp Koehn; Kevin Knight

W02-0508

A Morphological,Syntactic,and Semantic Search Engine for Hebrew Texts

Uzzi Ornan

W02-0502

Generating Hebrew Verb Morphology by Default Inheritance Hierarchies

Raphael Finkel; Gregory Stump